
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import seaborn as sns
from pandas.plotting import scatter_matrix


df = pd.read_excel("C:/Users/BharathThatipamula/Desktop/wine/Wine_data.xlsx")
df.head(5)


df.isnull().sum()

fixed acidity           0
volatile acidity        0
citric acid             0
residual sugar          0
chlorides               0
free sulfur dioxide     0
total sulfur dioxide    0
density                 0
pH                      0
sulphates               0
alcohol                 0
quality                 0
dtype: int64


df.describe()


df.dtypes

fixed acidity           float64
volatile acidity        float64
citric acid             float64
residual sugar          float64
chlorides               float64
free sulfur dioxide     float64
total sulfur dioxide    float64
density                 float64
pH                      float64
sulphates               float64
alcohol                 float64
quality                   int64
dtype: object


# Plot histograms for specified columns
plt.figure(figsize=(12, 8))

plt.subplot(3, 2, 1)
plt.hist(df['fixed acidity'], bins=30, color='slategray', edgecolor='black')
plt.title('Fixed Acidity')

plt.subplot(3, 2, 2)
plt.hist(df['volatile acidity'], bins=30, color='slategray', edgecolor='black')
plt.title('Volatile Acidity')

plt.subplot(3, 2, 3)
plt.hist(df['citric acid'], bins=30, color='slategray', edgecolor='black')
plt.title('Citric Acid')

plt.subplot(3, 2, 4)
plt.hist(df['residual sugar'], bins=30, color='slategray', edgecolor='black')
plt.title('Residual Sugar')

plt.subplot(3, 2, 5)
plt.hist(df['chlorides'], bins=30, color='slategray', edgecolor='black')
plt.title('Chlorides')

plt.tight_layout()
plt.show()


# Plot box plots for specified columns
plt.figure(figsize=(12, 8))

plt.subplot(3, 2, 1)
sns.boxplot(x='quality', y='fixed acidity', data=df, color='slategray')
plt.title('Fixed Acidity')

plt.subplot(3, 2, 2)
sns.boxplot(x='quality', y='volatile acidity', data=df, color='slategray')
plt.title('Volatile Acidity')

plt.subplot(3, 2, 3)
sns.boxplot(x='quality', y='citric acid', data=df, color='slategray')
plt.title('Citric Acid')

plt.subplot(3, 2, 4)
sns.boxplot(x='quality', y='residual sugar', data=df, color='slategray')
plt.title('Residual Sugar')

plt.subplot(3, 2, 5)
sns.boxplot(x='quality', y='chlorides', data=df, color='slategray')
plt.title('Chlorides')

plt.tight_layout()
plt.show()


# Summary statistics
summary = df.describe()
print(summary)

       fixed acidity  volatile acidity  citric acid  residual sugar  \
count    4898.000000       4898.000000  4898.000000     4898.000000   
mean        6.854788          0.278241     0.334192        6.391415   
std         0.843868          0.100795     0.121020        5.072058   
min         3.800000          0.080000     0.000000        0.600000   
25%         6.300000          0.210000     0.270000        1.700000   
50%         6.800000          0.260000     0.320000        5.200000   
75%         7.300000          0.320000     0.390000        9.900000   
max        14.200000          1.100000     1.660000       65.800000   

         chlorides  free sulfur dioxide  total sulfur dioxide      density  \
count  4898.000000          4898.000000           4898.000000  4898.000000   
mean      0.045772            35.308085            138.360657     0.994027   
std       0.021848            17.007137             42.498065     0.002991   
min       0.009000             2.000000              9.000000     0.987110   
25%       0.036000            23.000000            108.000000     0.991723   
50%       0.043000            34.000000            134.000000     0.993740   
75%       0.050000            46.000000            167.000000     0.996100   
max       0.346000           289.000000            440.000000     1.038980   

                pH    sulphates      alcohol      quality  
count  4898.000000  4898.000000  4898.000000  4898.000000  
mean      3.188267     0.489847    10.514267     5.877909  
std       0.151001     0.114126     1.230621     0.885639  
min       2.720000     0.220000     8.000000     3.000000  
25%       3.090000     0.410000     9.500000     5.000000  
50%       3.180000     0.470000    10.400000     6.000000  
75%       3.280000     0.550000    11.400000     6.000000  
max       3.820000     1.080000    14.200000     9.000000


# Correlation matrix
correlation_matrix = df.iloc[:, :-1].corr()
print(correlation_matrix)
df.head()

                      fixed acidity  volatile acidity  citric acid  \
fixed acidity              1.000000         -0.022697     0.289181   
volatile acidity          -0.022697          1.000000    -0.149472   
citric acid                0.289181         -0.149472     1.000000   
residual sugar             0.089021          0.064286     0.094212   
chlorides                  0.023086          0.070512     0.114364   
free sulfur dioxide       -0.049396         -0.097012     0.094077   
total sulfur dioxide       0.091070          0.089261     0.121131   
density                    0.265331          0.027114     0.149503   
pH                        -0.425858         -0.031915    -0.163748   
sulphates                 -0.017143         -0.035728     0.062331   
alcohol                   -0.120881          0.067718    -0.075729   

                      residual sugar  chlorides  free sulfur dioxide  \
fixed acidity               0.089021   0.023086            -0.049396   
volatile acidity            0.064286   0.070512            -0.097012   
citric acid                 0.094212   0.114364             0.094077   
residual sugar              1.000000   0.088685             0.299098   
chlorides                   0.088685   1.000000             0.101392   
free sulfur dioxide         0.299098   0.101392             1.000000   
total sulfur dioxide        0.401439   0.198910             0.615501   
density                     0.838966   0.257211             0.294210   
pH                         -0.194133  -0.090439            -0.000618   
sulphates                  -0.026664   0.016763             0.059217   
alcohol                    -0.450631  -0.360189            -0.250104   

                      total sulfur dioxide   density        pH  sulphates  \
fixed acidity                     0.091070  0.265331 -0.425858  -0.017143   
volatile acidity                  0.089261  0.027114 -0.031915  -0.035728   
citric acid                       0.121131  0.149503 -0.163748   0.062331   
residual sugar                    0.401439  0.838966 -0.194133  -0.026664   
chlorides                         0.198910  0.257211 -0.090439   0.016763   
free sulfur dioxide               0.615501  0.294210 -0.000618   0.059217   
total sulfur dioxide              1.000000  0.529881  0.002321   0.134562   
density                           0.529881  1.000000 -0.093591   0.074493   
pH                                0.002321 -0.093591  1.000000   0.155951   
sulphates                         0.134562  0.074493  0.155951   1.000000   
alcohol                          -0.448892 -0.780138  0.121432  -0.017433   

                       alcohol  
fixed acidity        -0.120881  
volatile acidity      0.067718  
citric acid          -0.075729  
residual sugar       -0.450631  
chlorides            -0.360189  
free sulfur dioxide  -0.250104  
total sulfur dioxide -0.448892  
density              -0.780138  
pH                    0.121432  
sulphates            -0.017433  
alcohol               1.000000


# Spearman correlation matrix
spearman_correlation_matrix = df.iloc[:, :-1].corr(method='spearman')
print(spearman_correlation_matrix)
df.head()

                      fixed acidity  volatile acidity  citric acid  \
fixed acidity              1.000000         -0.042865     0.297878   
volatile acidity          -0.042865          1.000000    -0.150410   
citric acid                0.297878         -0.150410     1.000000   
residual sugar             0.106725          0.108627     0.024621   
chlorides                  0.094691         -0.004934     0.032659   
free sulfur dioxide       -0.024542         -0.081213     0.088314   
total sulfur dioxide       0.112649          0.117614     0.093219   
density                    0.270031          0.010124     0.091425   
pH                        -0.418341         -0.045204    -0.146193   
sulphates                 -0.013238         -0.016902     0.079766   
alcohol                   -0.106827          0.033967    -0.029170   

                      residual sugar  chlorides  free sulfur dioxide  \
fixed acidity               0.106725   0.094691            -0.024542   
volatile acidity            0.108627  -0.004934            -0.081213   
citric acid                 0.024621   0.032659             0.088314   
residual sugar              1.000000   0.227844             0.346107   
chlorides                   0.227844   1.000000             0.167046   
free sulfur dioxide         0.346107   0.167046             1.000000   
total sulfur dioxide        0.431252   0.375244             0.618616   
density                     0.780365   0.508302             0.327822   
pH                         -0.180028  -0.054006            -0.006274   
sulphates                  -0.003844   0.093931             0.052252   
alcohol                    -0.445257  -0.570806            -0.272569   

                      total sulfur dioxide   density        pH  sulphates  \
fixed acidity                     0.112649  0.270031 -0.418341  -0.013238   
volatile acidity                  0.117614  0.010124 -0.045204  -0.016902   
citric acid                       0.093219  0.091425 -0.146193   0.079766   
residual sugar                    0.431252  0.780365 -0.180028  -0.003844   
chlorides                         0.375244  0.508302 -0.054006   0.093931   
free sulfur dioxide               0.618616  0.327822 -0.006274   0.052252   
total sulfur dioxide              1.000000  0.563824 -0.011829   0.157825   
density                           0.563824  1.000000 -0.110061   0.095079   
pH                               -0.011829 -0.110061  1.000000   0.140243   
sulphates                         0.157825  0.095079  0.140243   1.000000   
alcohol                          -0.476619 -0.821855  0.148857  -0.044868   

                       alcohol  
fixed acidity        -0.106827  
volatile acidity      0.033967  
citric acid          -0.029170  
residual sugar       -0.445257  
chlorides            -0.570806  
free sulfur dioxide  -0.272569  
total sulfur dioxide -0.476619  
density              -0.821855  
pH                    0.148857  
sulphates            -0.044868  
alcohol               1.000000


with sns.plotting_context("notebook", font_scale=1.2):
    scatter_matrix(df.iloc[:, :-1], alpha=0.7, figsize=(20, 20), diagonal='kde', marker='o', color='red')
    plt.suptitle("Scatterplot of Chemical Attributes", y=1.02, fontsize=10)
    plt.show()


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Preparing data for regression
X = df.drop('quality', axis=1)  # Features
y = df['quality']               # Target

# Splitting data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Multiple Linear Regression
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions and model evaluation
predictions = model.predict(X_test)
print('Mean Squared Error:', mean_squared_error(y_test, predictions))

Mean Squared Error: 0.5690247717275313


import seaborn as sns
import matplotlib.pyplot as plt

# Calculate the correlation matrix
correlation_matrix = df.corr()

# Create a heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)
plt.title('Correlation Matrix Heatmap')
plt.show()


from sklearn.preprocessing import PolynomialFeatures

# Creating polynomial features
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)

# Splitting the data
X_train_poly, X_test_poly, y_train, y_test = train_test_split(X_poly, y, test_size=0.2, random_state=42)

# Polynomial Regression
model_poly = LinearRegression()
model_poly.fit(X_train_poly, y_train)

# Predictions and evaluation
predictions_poly = model_poly.predict(X_test_poly)
print('Mean Squared Error for Polynomial Regression:', mean_squared_error(y_test, predictions_poly))

Mean Squared Error for Polynomial Regression: 0.6193710915884316


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Combine features and target for the training set
train_data = pd.concat([X_train, y_train], axis=1)

# Create a pair plot
sns.pairplot(train_data, vars=X.columns, markers='o', diag_kind='kde')
plt.suptitle("Pair Plot of Features and Target", y=1.02)
plt.show()


from sklearn.tree import DecisionTreeRegressor

# Decision Tree Regression
tree_model = DecisionTreeRegressor(random_state=42)
tree_model.fit(X_train, y_train)

# Predictions and evaluation
tree_predictions = tree_model.predict(X_test)
print('Mean Squared Error for Decision Tree:', mean_squared_error(y_test, tree_predictions))

Mean Squared Error for Decision Tree: 0.6602040816326531


from sklearn.ensemble import RandomForestRegressor

# Random Forest Regression
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

# Predictions and evaluation
rf_predictions = rf_model.predict(X_test)
print('Mean Squared Error for Random Forest:', mean_squared_error(y_test, rf_predictions))

Mean Squared Error for Random Forest: 0.34775581632653063


from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Converting the problem into a classification (optional: define thresholds for wine quality)
# For simplicity, we are using it as is.

# Splitting the data (classification)
X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X, y, test_size=0.2, random_state=42)

# Random Forest Classifier
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
rf_classifier.fit(X_train_class, y_train_class)

# Predictions and evaluation
class_predictions = rf_classifier.predict(X_test_class)
print(classification_report(y_test_class, class_predictions))

              precision    recall  f1-score   support

           3       0.00      0.00      0.00         5
           4       0.60      0.24      0.34        25
           5       0.70      0.69      0.70       291
           6       0.66      0.79      0.72       432
           7       0.76      0.58      0.66       192
           8       0.80      0.46      0.58        35

    accuracy                           0.69       980
   macro avg       0.59      0.46      0.50       980
weighted avg       0.69      0.69      0.68       980

C:\Users\BharathThatipamula\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\BharathThatipamula\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\BharathThatipamula\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

Exploratory Data Analysis

Project Overview

Prediction of Quality ranking from the chemical properties of the wines A predictive model developed on this data is expected to provide guidance to vineyards regarding quality and price expected on their produce without heavy reliance on the volatility of wine tasters

Importing Libraries

Exploratory Data Analysis (EDA) and Data Pre-processing

Pearson’s Correlation

Spearman’s Rank Correlation

Conclusion

It does not look like wine quality is well supported by its chemical properties. At each quality level variability of the predictors is high and the groups are not well separated.

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.0	0.27	0.36	20.7	0.045	45.0	170.0	1.0010	3.00	0.45	8.8	6
1	6.3	0.30	0.34	1.6	0.049	14.0	132.0	0.9940	3.30	0.49	9.5	6
2	8.1	0.28	0.40	6.9	0.050	30.0	97.0	0.9951	3.26	0.44	10.1	6
3	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6
4	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
count	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000
mean	6.854788	0.278241	0.334192	6.391415	0.045772	35.308085	138.360657	0.994027	3.188267	0.489847	10.514267	5.877909
std	0.843868	0.100795	0.121020	5.072058	0.021848	17.007137	42.498065	0.002991	0.151001	0.114126	1.230621	0.885639
min	3.800000	0.080000	0.000000	0.600000	0.009000	2.000000	9.000000	0.987110	2.720000	0.220000	8.000000	3.000000
25%	6.300000	0.210000	0.270000	1.700000	0.036000	23.000000	108.000000	0.991723	3.090000	0.410000	9.500000	5.000000
50%	6.800000	0.260000	0.320000	5.200000	0.043000	34.000000	134.000000	0.993740	3.180000	0.470000	10.400000	6.000000
75%	7.300000	0.320000	0.390000	9.900000	0.050000	46.000000	167.000000	0.996100	3.280000	0.550000	11.400000	6.000000
max	14.200000	1.100000	1.660000	65.800000	0.346000	289.000000	440.000000	1.038980	3.820000	1.080000	14.200000	9.000000

Exploratory Data Analysis

Project Overview

Prediction of Quality ranking from the chemical properties of the wines A predictive model developed on this data is expected to provide guidance to vineyards regarding quality and price expected on their produce without heavy reliance on the volatility of wine tasters

The following analytical approaches are taken: Multiple regression: The response Quality is assumed to be a continuous variable and is predicted by the independent predictors, all of which are continuous Regression Tree Classification of wines based on the chemical properties: Unsupervised analysis

Importing Libraries

Exploratory Data Analysis (EDA) and Data Pre-processing

Pearson’s Correlation

Spearman’s Rank Correlation

Conclusion

It does not look like wine quality is well supported by its chemical properties. At each quality level variability of the predictors is high and the groups are not well separated.